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--- |
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license: apache-2.0 |
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base_model: facebook/bart-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bart-noised-with-gcd-dist-0.3 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bart-noised-with-gcd-dist-0.3 |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0832 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 1.4395 | 0.11 | 500 | 1.3691 | |
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| 1.4568 | 0.21 | 1000 | 1.3068 | |
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| 1.3702 | 0.32 | 1500 | 1.2510 | |
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| 1.2945 | 0.43 | 2000 | 1.2319 | |
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| 1.3022 | 0.54 | 2500 | 1.2092 | |
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| 1.1775 | 0.64 | 3000 | 1.2022 | |
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| 1.1657 | 0.75 | 3500 | 1.1822 | |
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| 1.1756 | 0.86 | 4000 | 1.1823 | |
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| 1.2103 | 0.96 | 4500 | 1.1521 | |
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| 1.1467 | 1.07 | 5000 | 1.1535 | |
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| 1.0617 | 1.18 | 5500 | 1.1443 | |
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| 1.0667 | 1.28 | 6000 | 1.1339 | |
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| 1.0906 | 1.39 | 6500 | 1.1369 | |
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| 1.0492 | 1.5 | 7000 | 1.1213 | |
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| 1.0203 | 1.61 | 7500 | 1.1207 | |
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| 0.9431 | 1.71 | 8000 | 1.1183 | |
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| 1.0522 | 1.82 | 8500 | 1.1052 | |
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| 1.0673 | 1.93 | 9000 | 1.0996 | |
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| 0.9521 | 2.03 | 9500 | 1.1079 | |
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| 0.9615 | 2.14 | 10000 | 1.0990 | |
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| 0.996 | 2.25 | 10500 | 1.0976 | |
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| 0.9458 | 2.35 | 11000 | 1.0997 | |
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| 0.9375 | 2.46 | 11500 | 1.0939 | |
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| 0.8869 | 2.57 | 12000 | 1.0896 | |
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| 0.8934 | 2.68 | 12500 | 1.0895 | |
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| 0.8561 | 2.78 | 13000 | 1.0839 | |
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| 0.9795 | 2.89 | 13500 | 1.0834 | |
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| 0.8886 | 3.0 | 14000 | 1.0832 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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